Flowers, leaves or both? How to obtain suitable images for automated plant identification

Background: Deep learning algorithms for automated plant identification need large quantities of precisely labelled images in order to produce reliable classification results. Here, we explore what kind of perspectives and their combinations contain more characteristic information and therefore allow for higher identification accuracy. Results: We developed an image-capturing scheme to create observations of flowering plants. Each observation comprises five in-situ images of the same individual from predefined perspectives (entire plant, flower frontal- and lateral view, leaf top- and back side view). We collected a completely balanced dataset comprising 100 observations for each of 101 species with an emphasis on groups of conspecific and visually similar species including twelve Poaceae species. We used this dataset to train convolutional neural networks and determine the prediction accuracy for each single perspective and their combinations via score level fusion. Top-1 accuracies ranged between 77% (entire plant) and 97% (fusion of all perspectives) when averaged across species. Flower frontal view achieved the highest accuracy (88%). Fusing flower frontal, flower lateral and leaf top views yields the most reasonable compromise with respect to acquisition effort and accuracy (96%). The perspective achieving the highest accuracy was species dependent. Conclusions: We argue that image databases of herbaceous plants would benefit from multi organ observations, comprising at least the front and lateral perspective of flowers and the leaf top view

Deep learning in plant phenological research: A systematic literature review

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field

Towards more effective identification keys: A study of people identifying plant species characters

Abstract Accurate species identification is essential for ecological monitoring and biodiversity conservation. Interactive plant identification keys have been considerably improved in recent years, mainly by providing iconic symbols, illustrations, or images for the users, as these keys are also commonly used by people with relatively little plant knowledge. Only a few studies have investigated how well morphological characteristics can be recognized and correctly identified by people, which is ultimately the basis of an identification key's success. This study consists of a systematic evaluation of people's abilities in identifying plant‐specific morphological characters. We conducted an online survey where 484 participants were asked to identify 25 different plant character states on six images showing a plant from different perspectives. We found that survey participants correctly identified 79% of the plant characters, with botanical novices with little or no previous experience in plant identification performing slightly worse than experienced botanists. We also found that flower characters are more often correctly identified than leaf characteristics and that characters with more states resulted in higher identification errors. Additionally, the longer the time a participant needed for answering, the higher the probability of a wrong answer. Understanding what influences users' plant character identification abilities can improve the development of interactive identification keys, for example, by designing keys that adapt to novices as well as experts. Furthermore, our study can act as a blueprint for the empirical evaluation of identifications keys. Read the free Plain Language Summary for this article on the Journal blog

Unterirdische Kontinuität und Pilzvielfalt alter Waldstandorte

Alte Wälder und Waldstandorte besitzen insgesamt eine über der vergleichbarer, jüngerer Wälder liegende Organismenvielfalt. Die Wald- und Standortgeschichte beeinflusst die Waldbiodiversität massgeblich, und die Kontinuität von Lebensräumen ist essenziell für die Ausprägung dieser Biodiversität. Die unterirdische Kontinuität besteht aus diversen Teilaspekten, die jeder für sich Einfluss auf die Ausprägung der Biodiversität haben: Kontinuität der Kohlenstoff- und Stickstoffspeicher, der Oberflächenalterung (grosse terricole Moospolster oder Flechtenrasen), der ungestörten Bodenhydrologie, Kontinuität von natürlichen Prozessen des Stoffumsatzes wie Biound Kryoturbation, Kontinuität der Pedogenese und geologischen Schichtung sowie von unterirdischer und erdgebundener Strukturvielfalt. Es gibt zwei Ansätze, um die unterirdische Kontinuität über terrestrische Monitoringverfahren abschätzen zu können. Der eine Ansatz beruht auf der Erhebung bestimmter kontinuitätsanzeigender Pilzarten (sog. Signalarten), der andere auf Strukturmerkmalen, die auf Kontinuität schliessen lassen. Wir zeigen die Beziehung zwischen pilzlichen Kontinuitätszeigern und der Pilzartendiversität (ohne Signalarten) auf Probekreisebene (500 m²) mithilfe eines Kontinuitätsindizes und unter Verwendung eines Regressionsbaumes auf. Unsere Schlussfolgerungen sind, dass 1) es ein hohes Schutzgebot für Wälder langer Kontinuität geben sollte, da ihre hoch spezialisierte Pilzartenvielfalt deutlich zur biologischen Vielfalt beiträgt, und 2) die unterirdische Kontinuität im Waldmonitoring stärker berücksichtigt werden sollte

Automated plant species identification—Trends and future directions

Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts

Patch patterns of lowland beech forests in a gradient of management intensity

Forest development phases (FDPs) represent patches that are in different stages of the forest life cycle as conceived in the shifting mosaic concept. FDPs are a widely used framework to describe forest stand structure and dynamics. Natural forests are characterized by small patch sizes, a full set of FDPs and a large vertical heterogeneity which is considered crucial for their biodiversity. Forest management approaches that promote such characteristics of high naturalness are increasingly recommended for biodiversity conservation. Here we investigate the effect of a 10-year naturalness-promoting management regime on forest stand structure, expressed through different patterns in FDP structure and composition. We studied 22 beech forest stands in north-eastern Germany that are managed in two different ways (naturalness-promoting management and other management) or that have been unmanaged for varying periods of time (recently, 20–35 years and long-term, 65 to more than 100 years). FDPs were investigated in 2012/13 across the total area of the study sites (714 ha). The FDP assignment is based on a dichotomic decision tree with variables such as diameter at breast height, canopy cover, deadwood amount, regeneration cover and tree height. We analyzed FDP patch size, aggregation and mean minimum distance between patches of the same FDP and structural evenness of FDP proportions. For stands with naturalness-promoting management we found that: (1) there are different FDP proportions, FDP patch sizes and distances between patches of the same FDP compared to the other three management types; (2) there are significant differences in comparison to long-term unmanaged stands in terms of the aggregation indices of the initial phase, optimum phases and the disintegration phase; (3) these stands have the highest aggregation of the regeneration phase, which differs significantly from the other management types; and (4) they contain a similar FDP distribution to that in natural beech forests. In conclusion, naturalness-promoting management supports small-scale patch heterogeneity and maintains forest structure and life cycle that are closer to natural and unmanaged stands compared to other management types

Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

Background: Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. Methods: In this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination. Results: The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf’s top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf’s boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. Conclusions: In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this study provides a systematic guideline allowing researchers to spend available acquisition resources wisely while yielding the optimal classification accuracy

Increasing ecological multifunctionality during early plant succession

Ecological multifunctionality quantifies the functional performance of various important plant traits and increases with growing structural habitat heterogeneity, number of plant functional traits, and species richness. However, the successional changes in multifunctionality have not been traced so far. We use quantitative plant samples of 1 m2 plots from the first 6 years of initial vegetation dynamics in a German created catchment to infer the temporal changes in plant functional trait space and multifunctionality. Multifunctionality at the plot level was in all study years lower than expected from a random sample of the local pool of potential colonizers and was lowest at intermediate states of succession. In each year species containing a specific set of traits occurred with limited but focused functionality. The observed average low degree of multifunctionality contrasts with recent models predicting a tendency towards maximum multifunctionality during plant community development. However, variability in multifunctionality among plots increased during succession and the respective multifunctionality distribution among plots was increasingly right skewed indicating an excess of plots with relatively high multifunctionality. This relative excess of plots with high multifunctionality might act as an important trigger of community development paving the way for new species and functions to become established